Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Correspondence
  • Published:

ATAC Primer Tool for targeted analysis of accessible chromatin

Nature Methods volume 15, pages 304–305 (2018)Cite this article

Subjects

To the Editor:

Many fragments generated by Tn5 transposition during ATAC-seq are less than 100 base pairs long and thus are too short for quantification by qPCR. To overcome this limitation, APT identifies optimal regions for ATAC-qPCR primers within peaks by comparing the number of spanning fragments in overlapping windows to the normalized peak height across samples (Fig. 1b, Supplementary Table 1).

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: ATAC Primer Tool enables targeted and allele-specific quantification by ATAC-qPCR.

References

  1. Buenrostro, J.D., Giresi, P.G., Zaba, L.C., Chang, H.Y. & Greenleaf, W.J. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  Google Scholar 

  2. Xu, J. et al. Nat. Genet. 49, 377–386 (2017).

    Article  CAS  Google Scholar 

  3. Huang, F.W. et al. Science 339, 957–959 (2013).

    Article  CAS  Google Scholar 

  4. Stern, J.L., Theodorescu, D., Vogelstein, B., Papadopoulos, N. & Cech, T.R. Genes Dev. 29, 2219–2224 (2015).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank members of the Chang and Greenleaf labs for discussion. This work was supported by the NIH (grant P50-HG007735 to H.Y.C.), the National Science Foundation (Graduate Research Fellowship DGE-1656518 to K.E.Y.) and Stanford University (Graduate Fellowships to K.E.Y. and A.C.C.).

Author information

Authors and Affiliations

  1. Center for Personal Dynamic Regulomes, Stanford University, Stanford, California, USA

    Kathryn E Yost, Ava C Carter, Jin Xu, Ulrike Litzenburger & Howard Y Chang

Authors
  1. Kathryn E Yost
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ava C Carter
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ulrike Litzenburger
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Howard Y Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Howard Y Chang.

Ethics declarations

Competing interests

H.Y.C. is a founder of Accent Therapeutics and Epinomics and is a consultant for Spring Discovery. Stanford University has filed a patent on ATAC-seq, on which H.Y.C. is named as an inventor.

Integrated supplementary information

Supplementary Figure 1 ATAC Primer Tool workflow and Galaxy interface.

(a) Detailed overview of ATAC Primer Tool workflow for identification of optimal ATAC-qPCR primer locations. (b) Screenshot of the APT Galaxy Tool interface. Users upload coordinates of peaks of interest in BED file format and ATAC-seq BAM files. The Galaxy workflow outputs the coordinates and sequences of optimal ATAC-qPCR primer regions.

Supplementary Figure 2 Identification of universal normalization controls across ENCODE ATAC-seq samples and workflow for identification of cell-type-specific ATAC-qPCR normalization peaks.

(a) Distribution of log2 normalized counts ratios of all ATAC peaks and universal normalization controls (AK5 promoter and KIF26B promoter) in human ENCODE ATAC-seq samples. All ratios in (a) and (c) calculated relative to adipose tissue. (b) Distribution of log2 normalized counts ratios of all ATAC peaks and universal normalization controls (Kif3b promoter and Rpl12 promoter) in mouse ENCODE ATAC-seq samples. All ratios in (b) and (d) calculated relative to activated Tregs (1). (c) Variation in log2 normalized counts ratios of all ATAC peaks and universal normalization controls (AK5 promoter and KIF26B promoter) across human ENCODE ATAC-seq samples. (d) Variation in log2 normalized counts ratios of all ATAC peaks and universal normalization controls (Kif3b promoter and Rpl12 promoter) across mouse ENCODE ATAC-seq samples. (e) Detailed overview of workflow for identification of ATAC-qPCR normalization peaks for cell-type specific applications or additional species.

Supplementary Figure 3 Effect of cell-type-specific and universal normalization on correlation between ATAC-qPCR and ATAC-seq.

(a) Experimental design for ATAC-seq analysis of differentially accessible peaks in human BJ fibroblast cells time course of etoposide treatment. Cells were treated for 1, 2 or 6 hours. Differential peaks were called relative to DMSO treated cells. (b) Comparison of correlation between ATAC-seq and ATAC-qPCR of differentially accessible loci in human fibroblasts without normalization or following normalization to cell type specific and universal normalization controls. Plots include data from BJ fibroblasts treated with etoposide or DMSO, as well as BJ fibroblasts treated with JQ1 or THZ1, for the time points indicated in (a). (c) Experimental design for ATAC-seq analysis of differentially accessible peaks in mouse neural progenitor cells. Clonal NPC cell lines were derived from hybrid NPC cells and randomly monoallelic elements with differential accessibility between clones were identified. (d) Comparison of correlation between ATAC-seq and ATAC-qPCR of differentially accessible loci in parental mouse embryonic stem cells and neural progenitor cells without normalization or following normalization to cell type specific and universal normalization controls.

Source data

Supplementary Figure 4 Analysis of allele-specific accessibility with ATAC-PCR.

(a) Experimental design for generation of hybrid mouse NPC cell lines and calculation of d-score metric used for analysis of allelespecific accessibility. (b) Comparison of ATAC-seq and ATAC-qPCR for the randomly monoallelic element located at the Fam111a promoter. Three different clones are shown to illustrate biallelic, Cast specific and F129 specific accessibility. In the ATAC-seq tracks, total ATAC-seq reads are shown in grey, Cast reads are shown in blue, and 129 reads are shown in pink. (c) Correlation between ATAC-seq and ATAC-PCR quantification of allele-specific accessibility at a panel of five randomly monoallelic accessible sites in mouse neural progenitor cells.

Source data

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4, Supplementary Tables 1–7 and Supplementary Methods (PDF 3071 kb)

Life Sciences Reporting Summary (PDF 67 kb)

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yost, K., Carter, A., Xu, J. et al. ATAC Primer Tool for targeted analysis of accessible chromatin. Nat Methods 15, 304–305 (2018). https://doi.org/10.1038/nmeth.4663

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.4663

This article is cited by

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics